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Volume 29
Adv. Geosci., 29, 51–59, 2011
https://doi.org/10.5194/adgeo-29-51-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Geosci., 29, 51–59, 2011
https://doi.org/10.5194/adgeo-29-51-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Feb 2011

28 Feb 2011

A hydrologic post-processor for ensemble streamflow predictions

L. Zhao1, Q. Duan2, J. Schaake3,*, A. Ye2, and J. Xia1 L. Zhao et al.
  • 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • 2College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
  • 3NOAA/National Weather Service, Silver Spring, MD, USA
  • *retired

Abstract. This paper evaluates the performance of a statistical post-processor for imperfect hydrologic model forecasts. Assuming that the meteorological forecasts are well-calibrated, we employ a "General Linear Model (GLM)" to post-process simulations produced by a hydrologic model. For a particular forecast date, the observations and simulations from an "analysis window" and hydrologic model forecasts for a "forecast window", the GLM Post-Processor (GLMPP) is used to produce an ensemble of predictions of the streamflow observations that will occur during the "forecast window". The objectives of the GLMPP are to: (1) preserve any skill in the original hydrologic ensemble forecast; (2) correct systematic model biases; (3) retain the equal-likelihood assumption for the ensemble; (4) preserve temporal scale dependency relationships in streamflow hydrographs and the uncertainty in the predictions; and, (5) produce reliable ensemble predictions.

Observed and simulated daily streamflow data from the Second Workshop on Model Parameter Estimation Experiment (MOPEX) are used to test how well these objectives are met when the GLMPP is applied to ensemble hydrologic forecasts driven by well calibrated meteorological forecasts. A 39-year hydrologic dataset from the French Broad basin is split into calibration and verification periods. The results show that the GLMPP built using data from the calibration period removes the mean bias when applied to hydrologic model simulations from both the calibration and verification periods. Probability distributions of the post-processed model simulations are shown to be closer to the climatological probability distributions of observed streamflow than the distributions of the unadjusted simulated flows. A number of experiments with different GLMPP configurations were also conducted to examine the effects of different configurations for forecast and analysis window lengths on the robustness of the results.

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